Akash Nagaraj
Graduate Research Assistant
Providence, Rhode Island, United States
Summary
👤
Senior🎓
Top SchoolAkash Nagaraj is a research-focused software engineer and Graduate Research Assistant at Brown University with nine years of industry and research experience bridging AI, computer vision, and real-world systems. He develops task-optimized neural models of early visual cortex at the Serre Lab while leveraging strong programming skills in Python, C, R, and Java and experience with production-grade ML and full-stack systems from roles at Goldman Sachs and Cisco. His work spans low-latency trading platforms that handled multibillion-dollar daily flows to ML-driven failure analysis and process automation, demonstrating an ability to move models from research into robust, high-throughput applications. An active open-source contributor, he has improved SymPy’s physics/vector documentation to make complex reference-frame examples clearer for users. Comfortable in interdisciplinary teams, he seeks impact-oriented projects that combine computational vision and biology to solve practical problems.
9 years of coding experience
1 year of employment as a software developer
Bachelor’s Degree, Computer Science, 9.1, Bachelor’s Degree, Computer Science, 9.1 at PES UniversityMaster of Science - MS, Computer Science, 4.00, Master of Science - MS, Computer Science, 4.00 at Brown UniversityICSE, 10th Grade, 93%, ICSE, 10th Grade, 93% at Clarence High School BangaloreCBSE, 12th Grade, 91%, CBSE, 12th Grade, 91% at National Public School
English, Hindi, Telugu, Kannada, Tamil
Github Skills (6)
python10
documentation10
computer-algebra-system9
computer-algebra9
mathematics8
math8
Programming languages (8)
Github contributions (5)
sympy/sympy
Oct 2019 - Oct 2019
A computer algebra system written in pure Python
Role in this project:
Technical Writer
Technical WriterContributions:16 commits, 2 PRs, 25 comments in 1 day
Contributions summary:Akash primarily contributed to the documentation of the `sympy/sympy` repository. Their commits focused on updating and expanding docstrings within the `physics/vector` module, specifically related to `ReferenceFrame` and `Point` classes. The updates included incorporating examples to improve clarity for creating and using multiple reference frames and points, improving overall usability.
computer-algebra-systempythonmathcomputer-algebrascience
grassknoted/CDAE4InfoExtraction
Oct 2018 - Jan 2020
[WIP] Cross-domain Auto-encoders for Information Extraction, using Capsule Networks
Contributions:67 commits, 6 PRs, 51 pushes in 1 year 2 months
autoencoderinformation-extraction
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